{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes:\n",
    " * same as 6-2 but with no TOI data in training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/1/AstroCNNModelVetting_vrevised_tuned_20211023_235405',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/2/AstroCNNModelVetting_vrevised_tuned_20211024_001859',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/3/AstroCNNModelVetting_vrevised_tuned_20211024_004345',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/4/AstroCNNModelVetting_vrevised_tuned_20211024_010836',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/5/AstroCNNModelVetting_vrevised_tuned_20211024_013348',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/6/AstroCNNModelVetting_vrevised_tuned_20211024_020004',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/7/AstroCNNModelVetting_vrevised_tuned_20211024_022515',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/8/AstroCNNModelVetting_vrevised_tuned_20211024_025017',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/9/AstroCNNModelVetting_vrevised_tuned_20211024_031517',\n",
       " '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2/10/AstroCNNModelVetting_vrevised_tuned_20211024_034009']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "chkpt_root = '/mnt/tess/astronet/checkpoints/vrevised_tuned_6_notoi_run_2'\n",
    "data_files = '/mnt/tess/astronet/tfrecords-vetting-6-toi-val/*'\n",
    "tces_file = '/mnt/tess/astronet/tces-vetting-v5-toi-val.csv'\n",
    "\n",
    "nruns = 10\n",
    "\n",
    "def load_ensemble(chkpt_root, nruns):\n",
    "    checkpts = []\n",
    "    for i in range(nruns):\n",
    "        parent = os.path.join(chkpt_root, str(i + 1))\n",
    "        if not os.path.exists(parent):\n",
    "            break\n",
    "        all_dirs = os.listdir(parent)\n",
    "        if not all_dirs:\n",
    "            break\n",
    "        d, = all_dirs\n",
    "        checkpts.append(os.path.join(parent, d))\n",
    "    return checkpts\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model 1\n",
      "354 records\n",
      "Running model 2\n",
      "354 records\n",
      "Running model 3\n",
      "354 records\n",
      "Running model 4\n",
      "354 records\n",
      "Running model 5\n",
      "354 records\n",
      "Running model 6\n",
      "354 records\n",
      "Running model 7\n",
      "354 records\n",
      "Running model 8\n",
      "354 records\n",
      "Running model 9\n",
      "354 records\n",
      "Running model 10\n",
      "354 records\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "import os\n",
    "from astronet import predict\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "def run_predictions(path):\n",
    "    predict.FLAGS = predict.parser.parse_args([\n",
    "      '--model_dir', path,\n",
    "      '--data_files', data_files,\n",
    "      '--output_file', '',\n",
    "    ])\n",
    "\n",
    "    return predict.predict()\n",
    "\n",
    "\n",
    "paths = load_ensemble(chkpt_root, nruns)\n",
    "ensemble_preds = []\n",
    "config = None\n",
    "for i, path in enumerate(paths):\n",
    "    print(f'Running model {i + 1}')\n",
    "    preds, config = run_predictions(path)\n",
    "    ensemble_preds.append(preds.set_index('tic_id'))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['disp_p', 'disp_e', 'disp_n']\n",
    "\n",
    "col_p = labels.index('disp_p')\n",
    "thresh = 0.1579816417694639"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds_table = ensemble_preds[0].copy()\n",
    "all_preds_table['model'] = 0\n",
    "\n",
    "for i in range(len(ensemble_preds[1:])):\n",
    "    tmp = ensemble_preds[i].copy()\n",
    "    tmp['model'] = i\n",
    "    all_preds_table = all_preds_table.append(tmp)\n",
    "    \n",
    "all_preds_table.to_csv('notoi_ensemble_preds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "agg_preds = {}\n",
    "\n",
    "for preds in ensemble_preds:\n",
    "    for tic_id in preds.index:\n",
    "        if tic_id not in agg_preds:\n",
    "            agg_preds[tic_id] = []\n",
    "\n",
    "        row = preds[preds.index == tic_id]\n",
    "        pred_v = row.values[0]\n",
    "        if len(row.values) > 1:\n",
    "            print(f'Warning: duplicate predictions for {tic_id}')\n",
    "        if pred_v[col_p] >= thresh:\n",
    "            agg_preds[tic_id].append('disp_p')\n",
    "        else:\n",
    "            agg_preds[tic_id].append(preds.columns[np.argmax(pred_v)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_preds = []\n",
    "for tic_id in list(agg_preds.keys()):\n",
    "    counts = {l: 0 for l in labels}\n",
    "    for e in agg_preds[tic_id]:\n",
    "        counts[e] += 1\n",
    "    maxcount = max(counts.values())\n",
    "    counts.update({\n",
    "        'tic_id': tic_id,\n",
    "        'maxcount': maxcount,\n",
    "    })\n",
    "    final_preds.append(counts)\n",
    "    \n",
    "final_preds = pd.DataFrame(final_preds).set_index('tic_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tce_table = pd.read_csv(tces_file, header=0).set_index('tic_id')\n",
    "tce_labels = tce_table[labels]\n",
    "\n",
    "pl = final_preds.join(tce_labels, on='tic_id', how='left', lsuffix='_p')\n",
    "\n",
    "pl.head()\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.971830985915493\n",
      "Precision: 0.8117647058823529\n"
     ]
    }
   ],
   "source": [
    "ppos = (pl['disp_p_p'] > 0)\n",
    "pos = (pl['disp_p'] > 0)\n",
    "\n",
    "pneg = (pl['disp_p_p'] == 0)\n",
    "neg = (pl['disp_p'] == 0)\n",
    "\n",
    "print('Recall:', len(pl[ppos & pos]) / len(pl[pos]))\n",
    "print('Precision:', len(pl[ppos & pos]) / len(pl[ppos]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29984131\n",
      "283156726\n",
      "219208037\n",
      "121603086\n"
     ]
    }
   ],
   "source": [
    "for i in pl[pos & pneg][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119774374\n",
      "23826331\n",
      "24491836\n",
      "276645751\n",
      "276936320\n",
      "278225267\n",
      "278866211\n",
      "280095254\n",
      "30275619\n",
      "304729846\n",
      "307153959\n",
      "344978642\n",
      "380783252\n",
      "384068226\n",
      "384744828\n",
      "424876542\n",
      "98350979\n",
      "142963071\n",
      "137778682\n",
      "365494323\n",
      "388076435\n",
      "421154783\n",
      "149854222\n",
      "326974824\n",
      "262173985\n",
      "321828403\n",
      "24004619\n",
      "457138169\n",
      "255994336\n",
      "1715469662\n",
      "1715469667\n",
      "272783172\n"
     ]
    }
   ],
   "source": [
    "for i in pl[neg & ppos][[]].join(tce_table, on='tic_id', how='left').index:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.934916</td>\n",
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       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.912334</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.006362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.648020</td>\n",
       "      <td>0.336243</td>\n",
       "      <td>0.015737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.892884</td>\n",
       "      <td>0.090375</td>\n",
       "      <td>0.016741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.938803</td>\n",
       "      <td>0.049757</td>\n",
       "      <td>0.011440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.878660</td>\n",
       "      <td>0.114032</td>\n",
       "      <td>0.007308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.882939</td>\n",
       "      <td>0.100658</td>\n",
       "      <td>0.016403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.756971</td>\n",
       "      <td>0.223800</td>\n",
       "      <td>0.019230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.929319</td>\n",
       "      <td>0.055864</td>\n",
       "      <td>0.014817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>0.936495</td>\n",
       "      <td>0.047254</td>\n",
       "      <td>0.016251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             disp_p    disp_e    disp_n\n",
       "tic_id                                 \n",
       "113428669  0.934916  0.049846  0.015237\n",
       "113428669  0.912334  0.081304  0.006362\n",
       "113428669  0.648020  0.336243  0.015737\n",
       "113428669  0.892884  0.090375  0.016741\n",
       "113428669  0.938803  0.049757  0.011440\n",
       "113428669  0.878660  0.114032  0.007308\n",
       "113428669  0.882939  0.100658  0.016403\n",
       "113428669  0.756971  0.223800  0.019230\n",
       "113428669  0.929319  0.055864  0.014817\n",
       "113428669  0.936495  0.047254  0.016251"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compare(ensemble_preds, filter):\n",
    "    result = ensemble_preds[0][filter]\n",
    "    for preds in ensemble_preds[1:]:\n",
    "        result = result.append(preds[filter])\n",
    "    return result\n",
    "\n",
    "compare(ensemble_preds, preds.index == 113428669)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>disp_p_p</th>\n",
       "      <th>disp_e_p</th>\n",
       "      <th>disp_n_p</th>\n",
       "      <th>maxcount</th>\n",
       "      <th>disp_p</th>\n",
       "      <th>disp_e</th>\n",
       "      <th>disp_n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tic_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113428669</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           disp_p_p  disp_e_p  disp_n_p  maxcount  disp_p  disp_e  disp_n\n",
       "tic_id                                                                   \n",
       "113428669        10         0         0        10       1       0       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl[pl.index == 113428669]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PR curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tics = set(ensemble_preds[0].index.values)\n",
    "\n",
    "tic_index = {v: i for i, v in enumerate(tics)}\n",
    "\n",
    "pred_ps = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_es = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "pred_ns = np.zeros([len(ensemble_preds), len(tic_index)])\n",
    "for i, preds in enumerate(ensemble_preds):\n",
    "    for row in preds.iterrows():\n",
    "        tic, pred_p = row[0], row[1][col_p]\n",
    "        pred_ps[i][tic_index[tic]] = pred_p\n",
    "        pred_es[i][tic_index[tic]] = row[1][labels.index('disp_e')]\n",
    "        pred_ns[i][tic_index[tic]] = row[1][labels.index('disp_n')]\n",
    "\n",
    "lbl_ps = np.zeros([len(tic_index)], dtype=np.bool)\n",
    "bad_labels = []\n",
    "for row in tce_labels.iterrows():\n",
    "    tic, lbl_p = row[0], row[1]['disp_p']\n",
    "    if tic in tic_index:\n",
    "        lbl_ps[tic_index[tic]] = (lbl_p > 0)\n",
    "    else:\n",
    "        bad_labels.append(tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_cond_pos = float(np.sum(lbl_ps))\n",
    "\n",
    "def pr_at_th(th):\n",
    "    pred_pos = np.any(pred_ps >= th, axis=0)\n",
    "    true_pos = pred_pos & lbl_ps\n",
    "    num_pred_pos = int(np.sum(pred_pos))\n",
    "    num_true_pos = int(np.sum(true_pos))\n",
    "    if num_pred_pos == 0:\n",
    "        return 1.0, 0.0\n",
    "    return float(num_true_pos) / float(num_pred_pos), float(num_true_pos) / float(num_cond_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.9352318302797329, max R: 1.0, max P: 1.0\n",
      "100% recall at: 74%, threshold: 0.031026408672385597\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x740 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ps, rs, ths = ([], [], [])\n",
    "th = np.max(pred_ps)\n",
    "while th >= 0.0:\n",
    "    p, r = pr_at_th(th)\n",
    "    if p > 0:\n",
    "        ps.append(p)\n",
    "        rs.append(r)\n",
    "    else:\n",
    "        ps.append(1.0)\n",
    "        rs.append(0.0)\n",
    "    ths.append(th)\n",
    "    th -= 0.0005\n",
    "    \n",
    "from sklearn import metrics\n",
    "\n",
    "print(f'AUC: {metrics.auc(rs, ps)}, max R: {max(rs)}, max P: {max(ps)}')\n",
    "    \n",
    "i = len(rs) - 1\n",
    "while rs[i] == 1.0:\n",
    "    i -= 1\n",
    "i += 1\n",
    "print(f'100% recall at: {int(ps[i] * 100)}%, threshold: {ths[i]}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3.7), dpi=200)\n",
    "\n",
    "ax.spines['top'].set_color('#808080')\n",
    "ax.spines['right'].set_color('#808080')\n",
    "ax.spines['left'].set_color('#808080')\n",
    "ax.spines['bottom'].set_color('#808080')\n",
    "ax.tick_params(direction='in', color='#808080')\n",
    "\n",
    "plt.grid(color='#c0c0c0', linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.ylabel('Precision', fontweight='bold')\n",
    "plt.xlabel('Recall', fontweight='bold')\n",
    "\n",
    "plt.xlim((0.0, 1.0))\n",
    "plt.ylim((0.0, 1.0))\n",
    "\n",
    "_ = plt.plot(rs, ps, marker='.')\n",
    "_ = plt.plot([0.0, 1.0], [1.0, 0.0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
